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Elasticsearch announced they will end-of-life Swiftype in 2023 and are aggressively pushing to migrate customers to their own SaaS platform. But is it the best solution?

What is Swiftype?

Acquired by Elasticsearch in 2017, Swiftype was a simple, but powerful SaaS search solution built on top of Elasticsearch. While it had many fans, Elasticsearch has announced an end-of-life for Swiftype in 2023 and is actively encouraging customers to migrate to the Elasticsearch cloud.

While presumably Elasticsearch has made it easy for Swiftype customers to migrate, customers may want to consider some alternatives.


Swiftype grew in popularity early as an alternative to Google Site Search and Google Custom Search Engine, both of which have also been phased out by Google.

Elasticsearch launched its own hosted SaaS edition in 2018, which competed against Swiftype. Elasticsearch is known for its scalability and myriad plugins to extend search functionality for features such as machine learning capabilities.

While it’s a very powerful search engine for log analysis, Elasticsearch may not be the best solution for site search. Elasticsearch itself is built on top of Lucene which has certain limitations particularly as search indexes change.

Top Swiftype alternatives is a SaaS site search platform built from the ground up that offers tremendous flexibility and ease of configuration built on top of a cloud-native architecture for elastic scale.

While is also a new search engine, it approaches search very differently from Swiftype and other legacy search engines. treats search more like a database, which offers some advantages in near real-time read/write speed and data synchronization while avoiding costly re-indexing fees.

It also has built-in machine learning — and more specifically, reinforcement learning — for continuous improvement of search performance, and Neuralsearch for instant relevance. Unlike traditional search engines that have layered AI on top of their search algorithms, has included reinforcement learning as part of its core ranking algorithm. (For Algolia, layering AI on top is slightly problematic as the simplicity of the tie-breaking sort logic is totally nullified)

Core features, such as crawling, autocomplete, document indexing, synonyms, filters, faceted search, etc. are all baked in. In addition, offers a RESTful search API for connecting to business data and Node, PHP, and Go SDKs, and React and JavaScript libraries for complete front-end customization.

Notably, reviewers cited the lack of document indexing in Swiftype — a feature available natively in

Additionally, has taken a different approach to configuration with its relevance settings.

With relevance settings, you can configure the search algorithm to improve search relevance, set rules for how to prioritize certain results, or even A/B test different algorithms to determine which one provides the best search experience.

For example, if a customer is searching your electronics site for “phone cases” and you know the customer uses an Apple iPhone or is browsing via an iOS device, you can boost results for Apple-related products. features include:

  • Instant indexing with full-text crawler, including document (PDF, DOCX) search
  • Easy to add and configure advanced search capabilities such as typo tolerance, smart autosuggest, personalization, and AB testing via pipelines
  • User friendly drag and drop search design builder and ReactJS libraries
  • Search analytics for identifying search trends and opportunities

Best use cases:

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Like, Algolia is a new search engine built from the ground up. Originally, Algolia was developed for mobile search use cases, but has since been extended to more traditional search projects. Algolia can boast about its retrieval speed; it’s milliseconds faster than the competition. Those few milliseconds won’t matter for most use cases, but if speed is important, Algolia is worth a look.

Algolia features include:

  • Typo tolerant full-text search
  • Simple and easy to understand tie-breaking relevance algorithm
  • Very fast information retrieval

Best use cases:

  • Site search
  • App search
  • Mobile search



Coveo has built its own enterprise search technology that allows people to build secure, enterprise search applications and knowledge bases. With their tight coupling to SiteCore, Salesforce, ServiceNow, and other B2B enterprise applications and SQL and NoSQL databases, Coveo offers fast search across datastores for internal KBs and other enterprise use cases.

Coveo is not as general-purpose a search platform as or Swiftype. It’s a platform to ingest and transform different data types into searchable, accessible content with Coveo’s proprietary search, machine learning, and recommendations engines built on top. Reviewers have mentioned that initial indexing can be slow, that updates are equally slow and cumbersome, and the user experience and UI is average, but once your content is integrated into Coveo it can be a very powerful tool.

More recently, the company has acquired AI and e-commerce technology to extend its footprint into e-commerce use cases.

Coveo features include:

  • Machine learning-powered intelligent search
  • Recommendation and personalization features to deliver the right content faster to the right users across datastores
  • Native integrations with B2B services to provide comprehensive enterprise search

Best use cases:

  • Designed for mid-to-large enterprises with multiple systems and datastores to index
  • Backend search for internal knowledge base
  • E-commerce and site search



Lucidworks is an enterprise SaaS custom search solution built on top of Apache Solr, an open source search engine that’s powering thousands of enterprise search solutions. Effectively, customers get the power of Solr with a custom UI, APIs, and modules from Lucidworks.

Basic search features such as autocomplete and spell checking (what Lucidworks calls “query rewrites”) are premium features available for higher tier customers. If you’re looking for a professional services partner to help build a custom, powerful search solution, Lucidworks is worth a look.

Lucidworks features include:

  • Pre-built solutions for e-commerce, help desk, and knowledge management use cases
  • Lucidworks Fusion — personalized, federated search across enterprise datastores
  • Built-in NLP, categorization, and content clustering

Best use cases:

  • Large scale application and e-commerce search
  • Customer service
  • Knowledge base

Cloud search providers: Azure, AWS, and GCP

The major cloud providers now offer many alternatives to Swiftype and Elasticsearch including Microsoft’s Azure Cognitive Search, Amazon Cloudsearch, and Google Cloud Search. If you go the cloud provider route, you’re going to select the one your company is already working with.

Cloud providers offer both private and public hosted search solutions. If your app is hosted in one of these providers, then it might be worth considering them for your search service as well. Co-locating your search service with your app makes a lot of sense for reducing latency.

The pros and cons of each cloud service provider and software vary a lot. But they have some similarities:

  • Built to scale, but still require a good deal of hand-holding
  • Each solution requires expertise and overhead for managing the search instance
  • Best suited if you want to co-locate search with your site or app

Best use cases:

  • Enterprise search
  • Custom full-text search applications
  • Monitoring and analysis

Unlike the SaaS alternatives mentioned above, cloud search solutions are not drop-in replacements. They require considerable configuration and are typically only managed by engineering teams. The reason we’re including them on this list is because they can offer tremendous performance when co-located with your site.

We hope this article provides you with some good ideas for what solution to select for your use case. For more ideas, have a look at our Site Search Buyer’s Guide or blog on Best Practices for Site Search.

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